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dc.contributor.authorNovacek, Viten
dc.identifier.citationVit Novacek "Imprecise Empirical Ontology Refinement: Application to Taxonomy Acquisition", Proceedings of ICEIS 2007, vol. Artificial Intelligence and Decision Support Systems, INSTICC, 2007.en
dc.description.abstractThe significance of uncertainty representation has become obvious in the Semantic Web community recently. This paper presents new results of our research on uncertainty incorporation into ontologies created automatically by means of Human Language Technologies. The research is related to OLE (Ontology Learning) a ¿ a project aimed at bottom-up generation and merging of ontologies. It utilises a proposal of expressive fuzzy knowledge representation framework called ANUIC (Adaptive Net of Universally Interrelated Concepts). We discuss our recent achievements in taxonomy acquisition and show how even simple application of the principles of ANUIC can improve the results of initial knowledge extraction methods.en
dc.subjectOntology engineeringen
dc.subjectOntology learningen
dc.subjectTaxonomy acquisitonen
dc.subject.lcshKnowledge representation (Information theory)en
dc.subject.lcshConceptual structure (Information theory)en
dc.subject.lcshExpert systems (Computer science)en
dc.subject.lcshKnowledge acquisition (Expert systems)en
dc.subject.lcshKnowledge managementen
dc.subject.lcshUncertainty (Information theory)en
dc.titleImprecise Empirical Ontology Refinement: Application to Taxonomy Acquisitionen
dc.typeConference paperen
dc.contributor.funderKnowledge Weben

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